A critical cross-validation of high throughput structural binding prediction methods for pMHC |
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Authors: | Bernhard Knapp Ulrich Omasits Sophie Frantal Wolfgang Schreiner |
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Institution: | (1) Unit for Medical Statistics and Informatics—Section for Biomedical Computersimulation and Bioinformatics, Medical University of Vienna—General Hospital, Spitalgasse 23, Room: BT88—88.03.712, 1090 Wien, Austria;(2) Unit for Medical Statistics and Informatics—Section for Medical Statistics, Medical University of Vienna—General Hospital, Spitalgasse 23, 1090 Wien, Austria |
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Abstract: | T-cells recognize antigens via their T-cell receptors. The major histocompatibility complex (MHC) binds antigens in a specific
way, transports them to the surface and presents the peptides to the TCR. Many in silico approaches have been developed to
predict the binding characteristics of potential T-cell epitopes (peptides), with most of them being based solely on the amino
acid sequence. We present a structural approach which provides insights into the spatial binding geometry. We combine different
tools for side chain substitution (threading), energy minimization, as well as scoring methods for protein/peptide interfaces.
The focus of this study is on high data throughput in combination with accurate results. These methods are not meant to predict
the accurate binding free energy but to give a certain direction for the classification of peptides into peptides that are
potential binders and peptides that definitely do not bind to a given MHC structure. In total we performed approximately 83,000
binding affinity prediction runs to evaluate interactions between peptides and MHCs, using different combinations of tools.
Depending on the tools used, the prediction quality ranged from almost random to around 75% of accuracy for correctly predicting
a peptide to be either a binder or a non-binder. The prediction quality strongly depends on all three evaluation steps, namely,
the threading of the peptide, energy minimization and scoring. |
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Keywords: | T cell epitope prediction Scoring Energy minimization Threading Substitution |
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